Can Artificial Intelligence Exist without Data Science?
The days of sprawling excel sheets to collect and analyze data are far behind us, and no one is complaining. With the dawn of the age of the algorithm, today, we can analyze gargantuan volumes of data and gain intelligent insights in real-time.
We are fast approaching the time when CPU’s will reach the processing power of the human brain!
The progress in the data realm has ushered us into the era when futuristic technologies become more mainstream than ever before. Today, cognitive technologies, such as AI are being embraced within the enterprise at a rapid pace. You have government initiatives such as the Task Force on Artificial Intelligence for Economic Transformation set up under the National Institution for Transforming India (NITI) Aayog plan to help countries such as India leverage the power of this transformational technology.
Reports also show the growing interest in AI with estimated global spending on this technology to reach $77.6 billion in 2022, nearly three times the $24.0 billion forecast for 2018!
AI skills are also amongst the most in-demand skills for 2020 as the world becomes more AI-enabled.
At the same time, the rising interest in data science has also reached a crescendo. As organizations move towards becoming data-driven, they are looking at hiring more data scientists than ever before.
The number of data science job postings has increased by about 32% year over year. Today, data science is a high-demand skill, and the job of the data scientist has become ‘the’ job to have. Apart from the rise of the data economy, the rise of technologies such as AI and Machine Learning are crucial factors that have fuelled this growth.
Calling out AI experts – Are they all you need?
We can no longer ignore the power and the allure of AI. Reports show that this technology could add $15.7 trillion to the global economy by 2030. It promises to boost the global GDP by 14% in this time period. The limitless applicability of AI and its affiliate technologies, such as machine learning, deep learning, etc. make this technology extremely compelling. There is hardly any industry that is not attracted by the lure of AI.
But before you send out the clarion call for AI experts and set out on your AI initiatives, you need to assess are you looking at AI and data science in isolation? And also, are you thinking of AI and data science as one and the same, and using them interchangeably?
Data Science – The enabler of AI
While data science and AI seem conceptually similar, data science employs mathematics and statistics and uses techniques such as data mining, cluster analysis, visualizations, and even machine learning (a subset of AI) to enable data-driven decision making.
AI is all about employing data to create systems that can self-correct, work, and react like humans. It represents ‘perception->planning->action->feedback_perception’ loop. And data science happens to be a big enabler of this loop.
AI employs a class of data-driven algorithms that enable software applications to become highly accurate to predict outcomes without needing any explicit programming. The AI experts create algorithms that receive a data input and employ statistical models to predict the output, and also update the outputs as new data becomes available. If you say this sounds a lot like predictive modeling, you’re right. Because this process has a lot of commonality between predictive modeling and data mining and involves sifting through large data volumes to identify patterns and tweak the statistical models.
Data Science involves collecting, cleaning, and transforming data to analyze patterns that hide intelligent insights. AI then becomes one of the tools that are used to sift through these gargantuan volumes of data and analyze it. Many organizations are looking at AI experts to fill positions like Deep Learning or NLP scientists, etc., requirements that are mostly for creating products that breathe life into AI products. However, invariably, these roles need to employ data science tools like Python and R to perform data operations.
AI success depends on data collection, structuring, and modeling. This last step of ensuring that the models are effectively and efficiently operationalized is often neglected and yet is critical for success. You might build a great self-learning AI algorithm, but along with that, you need strong data pipelines that deliver regular and reliable data to them to drive and automate decision-making. In its absence, all you’ll have is a great algorithm. But you won’t be able to use it for your business.
Data science becomes integral to AI as:
· Data science helps identify the right data, find where it is located, assess what data to use, manipulate the data, and evaluate how it is structured
· Data science is what helps you understand how to extract value from data
· Data science deals with real-world complexity and has to be leveraged to come up with AI applications to solve real-world problems
· Data science is essential to create actual products that leverage AI capabilities
That being said, it becomes clear that we can’t, or rather, we shouldn’t look at AI and Data Science in isolation.
These two are interdisciplinary fields that need each other to deliver insights that drive business outcomes. The success of AI initiatives depends on creating the data pipeline to extract maximum value from the data assets at hand. And without data science, creating such as a pipeline that is dependable, robust, and fool-proof is quite impossible.